Big Data often involves a form of distributed storage and processing using Hadoop and MapReduce. One reason for this is
A) centralized storage creates too many vulnerabilities.
B) the "Big" in Big Data necessitates over 10,000 processing nodes.
C) the processing power needed for the centralized model would overload a single computer.
D) Big Data systems have to match the geographical spread of social media.

Question

Quizplus · Accepted Answer

The processing power required for handling Big Data is huge and cannot be handled by a single computer. A distributed storage and processing model like Hadoop and MapReduce allow for processing to be spread across multiple nodes, which provides the required computational power to handle Big Data. Centralized storage creates too many vulnerabilities and is not suitable for Big Data. The "Big" in Big Data does not necessarily mean over 10,000 processing nodes are required, and Big Data systems don't have to match the geographical spread of social media.

Big Data Often Involves a Form of Distributed Storage and Processing